Face hallucination is widely used in many applications, such as, surveillance, face recognition, face expression estimation and face age estimation. For the super-resolution for face images including the concerns for generic images, the face images have a unified structure which people are very familiar with. Even only few reconstruction errors occurring in a face image can cause visually annoying artifacts. For example, geometry distortion in the mouth and eyes on a reconstructed face image may only reduce the image's objective quality slightly, whereas the subjective quality of the reconstructed face can be degraded significantly.
Due to the structure characteristic of a human face, the face hallucination technology is developed. For example, one known prior art disclosed a generic image hallucination method. By capturing the primal sketch prior data in an input image with low resolution (LR), the method obtains a high resolution details corresponding to the input LR image. Another known prior art disclosed a method for soft edge smoothness and application on alpha channel super resolution, which processes a low resolution image by performing a high resolution edge segment extraction on the low resolution image, and generates a high quality image from a low quality image.
In one published paper titled “Image Hallucination Using Neighbor Embedding over Visual Primitive Manifolds”, it proposed a learning-based image hallucination method. By extracting the primitive features of the images and combining a plurality of training-concentrated primitive features, this schema obtains the high resolution primitive features of a target image. In another published paper titled “LPH super-resolution and neighbor reconstruction for residue compensation”, it proposed a two-phase face hallucination technology using manifold learning characteristics, i.e., every input image has the similar distribution in manifold domain. Accordingly, through computing LR image to patch the linear combination coefficients in the manifold domain, the technology then uses the same linear combination coefficients and radial basis function to obtain a high resolution image. This technology uses manifold learning to compose high resolution image.
Yet in another published paper titled “An example-based face hallucination method for single-frame, low resolution facial images”, it proposed an example-based face hallucination method. By using principal components analysis (PCA), this method decomposes LR images, uses as basis image for training, and matches human face through warping. As shown in an exemplary flowchart of FIG. 1, the technology computes the high resolution human face via computing the linear combination of a LR basis image and using the same combination in the high resolution basis image.
Yet in another published paper titled “Face hallucination using OLPP and Kernel Ridge Regression”, a face hallucination method is proposed. By using Orthogonal Locality Preserving Projection (OLPP) method of manifold learning, this method performs the dimension reduction on small segments of human face and uses probability model in a low dimensional subspace to estimate the high resolution small segment with maximum possibility. Then, the kernel ridge regression (KRR) prediction model is used to modify the reconstructed face.